'Error bounds' diaporamas de présentation

Backward Error Analysis and Numerical Software

Backward Error Analysis and Numerical Software. Sven Hammarling NAG Ltd, Oxford sven@nag.co.uk. Plan of Talk. Introductio n Stability Error analysis Condition The Basic Linear Algebra Subprograms ( BLAS ) LAPACK : state of the art L inear A lgebra PACK age

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19.5 Numeric Integration and Differentiation

19.5 Numeric Integration and Differentiation.

645 views • 42 slides

19.5 Numeric Integration and Differentiation

19.5 Numeric Integration and Differentiation.

515 views • 42 slides

Exploiting Similarity of Subqueries for Complex Query Optimization [1]

COSC6421 Advanced Database Systems Instructor Jarek Gryz. Exploiting Similarity of Subqueries for Complex Query Optimization [1]. Presented by Qiong Wang York University. Outline. Introduction-Why exploiting similarity

333 views • 33 slides

Near-Minimax Optimal Learning with Decision Trees

Near-Minimax Optimal Learning with Decision Trees. Rob Nowak and Clay Scott. University of Wisconsin-Madison and Rice University. nowak@engr.wisc.edu. Supported by the NSF and the ONR. Basic Problem. Classification : build a decision rule based on labeled training data.

407 views • 27 slides

SVM Support Vectors Machines

SVM Support Vectors Machines. Based on Statistical Learning Theory of Vapnik, Chervonenkis, Burges, Scholkopf, Smola, Bartlett, Mendelson, Cristianini Presented By: Tamer Salman. The addressed Problems. SVM can deal with three kinds of problems: Pattern Recognition / Classification.

409 views • 22 slides

Chapter 3 Interpolation and Polynomial Approximation

Chapter 3 Interpolation and Polynomial Approximation. 4.3 Lagrange Approximation. 1.3.1 Error Terms and Error Bounds. 1.3.2 Comparison of Accuracy and O(h N+1 ). function [C,L]=lagran(X,Y) w=length(X); n=w-1; L=zeros(w,w); for k=1:n+1 V=1; for j=1:n+1 if k~=j

345 views • 21 slides

Multiple Sequence Alignment

Multiple Sequence Alignment. Vasileios Hatzivassiloglou University of Texas at Dallas. Center star algorithm for multiple sequence global alignment. T is the set of strings that we want to align Pick S ? T that minimizes The initial alignment starts with S ( ? S 1 )

441 views • 21 slides

ApproxHadoop Bringing Approximations to MapReduce Frameworks

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Computing Sketches of Matrices Efficiently & (Privacy Preserving) Data Mining

Computing Sketches of Matrices Efficiently & (Privacy Preserving) Data Mining. Petros Drineas Rensselaer Polytechnic Institute drinep@cs.rpi.edu (joint work with R. Kannan and M. Mahoney) @ DIMACS Workshop on Privacy Preserving Data Mining. Motivation (Data Mining).

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Conservation of Momentum LAB 8

390 views • 18 slides